{"id":"W4287773581","doi":"10.1021/acsami.0c15612.s001","title":"Electrospun nanodiamond-silk fibroin membranes: a multifunctional\\n platform for biosensing and wound healing applications","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Diamond and Carbon-based Materials Research","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"RMIT University; ARC Centre for Nanoscale BioPhotonics; Ontario Ministry of Natural Resources and Forestry; South Australian Health and Medical Research Institute; Australian National Fabrication Facility","keywords":"Nanodiamond; Membrane; Fibroin; SILK; Materials science; Electrospinning; Wound healing; Nanofiber; Biocompatibility; Biomedical engineering; Nanotechnology; Nanoscopic scale; Fluorescence; Composite material; Chemistry; Polymer; Medicine; Surgery","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003973649,0.000358489,0.0004714659,0.0001978524,0.0003502968,0.0003344035,0.0004351065,0.0003053415,0.00005835151],"category_scores_gemma":[0.00007544448,0.0003994923,0.0001704499,0.0002959007,0.0002203877,0.0002219132,0.0005272232,0.0003036934,0.00006364634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000198144,"about_ca_system_score_gemma":0.0003338613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002408656,"about_ca_topic_score_gemma":0.00005969659,"domain_scores_codex":[0.9977225,0.00007232299,0.0003054744,0.00120054,0.0001491329,0.0005500161],"domain_scores_gemma":[0.9984971,0.0003149853,0.000227865,0.0004880129,0.0001831895,0.0002888897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006771463,0.000124825,0.0001575616,0.001782559,0.00009046256,0.00005152899,0.0001687033,0.01338337,0.9603969,0.02212927,0.0005301571,0.0005074827],"study_design_scores_gemma":[0.004416315,0.0005098347,0.0002641135,0.0005528553,0.000541905,0.00003213306,0.0008019903,0.2407414,0.6145971,0.1213119,0.0136734,0.00255702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.924073,0.0002616197,0.07173448,0.0002181475,0.0004185216,0.001716001,0.0004897855,0.0003788926,0.0007095022],"genre_scores_gemma":[0.9965932,0.000220505,0.002076207,0.0001341018,0.0002800527,0.00002829376,0.0002091631,0.00005079251,0.0004077282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3457999,"threshold_uncertainty_score":0.9998457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07552395185433355,"score_gpt":0.2245413674469838,"score_spread":0.1490174155926502,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}